# required libraries
library("RStoolbox")
library("raster")
library("rgdal")
library("ggplot2")
library("SDMTools")
library("png")

En este caso se eligieron 2 imágenes cada una con 2 parcelas distintas

# load tif using stack function instead raster
TTC08226_modified.stack <- stack("../6_qgis/output/TTC08226_modified.tif")
class(TTC08226_modified.stack)
[1] "RasterStack"
attr(,"package")
[1] "raster"
TTC08350_modified.stack <- stack("../6_qgis/output/TTC08350_modified.tif")
class(TTC08350_modified.stack)
[1] "RasterStack"
attr(,"package")
[1] "raster"
# load parcela 4
library(rgeos)
rgeos version: 0.3-26, (SVN revision 560)
 GEOS runtime version: 3.6.1-CAPI-1.10.1 r0 
 Linking to sp version: 1.2-5 
 Polygon checking: TRUE 
# ojo cambio de id entre parcela4 y parcela4.df
parcela4.df <- fortify(parcela4) # to plot with ggplot
Regions defined for each Polygons
head(parcela4.df)
parcela4_1 <- subset(parcelas, parcelas@data$id %in% c(7,8))
parcela4_1.df <- fortify(parcela4_1) # to plot with ggplot
Regions defined for each Polygons
parcela4_2 <- subset(parcelas, parcelas@data$id %in% c(5,6))
parcela4_2.df <- fortify(parcela4_1) # to plot with ggplot
Regions defined for each Polygons
# Set all pixels to NA, where bands are 0 (remove black background)
# Check if results are affected
# instead use crop and mask together (ver más adelante)
TTC08226_modified.stack[TTC08226_modified.stack[,] == 0] <- NA
# plot scene using ggRGB (from ggplot and RStoolbox)
ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = parcela4_1, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    geom_text(data = p4.centroid.df[3:4,], aes(label = c("4.1", "4.2") ,y = y, x = x), colour = "white") +
    coord_equal() +
    theme_bw()
Regions defined for each Polygons

ggsave("figures/parcela4N.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot parcel 4.1 (de norte a sur)

p41 <- subset(parcela4.df, id == 6)
ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p41, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Plot parcel 4.2

p42 <- subset(parcela4.df, id == 7)
ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p42, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

# Set all pixels to NA, where bands are 0 (remove black background)
# Check if results are affected
# instead use crop and mask together (ver más adelante)
TTC08350_modified.stack[TTC08350_modified.stack[,] == 0] <- NA
# plot scene using ggRGB (from ggplot and RStoolbox)
ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = parcela4_2, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    geom_text(data = p4.centroid.df[1:2,], aes(label = c("4.3", "4.4") ,y = y, x = x), colour = "white") +
    coord_equal() +
    theme_bw()
Regions defined for each Polygons

ggsave("figures/parcela4S.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot parcel 4.3

p43 <- subset(parcela4.df, id == 4)
TTC08350_modified.stack[TTC08350_modified.stack[,] == 0] <- NA
ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = p43, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

p44 <- subset(parcela4.df, id == 5)
ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p44, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Extraemos las parcelas

parcela 4N

# crop and mask whole area
TTC08226_modified.stack_p4N <- crop(mask(TTC08226_modified.stack, parcela4_1),parcela4_1)

Crop parcela 4N

# crop parcela 4N 1
parcela41 <- subset(parcelas, parcelas@data$id %in% c(7))
TTC08226_modified.stack_p41 <- crop(mask(TTC08226_modified.stack_p4N, parcela41), parcela41)
# crop parcela 4N 2
parcela42 <- subset(parcelas, parcelas@data$id %in% c(8))
TTC08226_modified.stack_p42 <- crop(mask(TTC08226_modified.stack_p4N, parcela42), parcela42)
 

Plot parcel 4N 1

# cambio de id !! 7 es igual a 6
p41 <- subset(parcela4.df, id == 6)
ggRGB(TTC08226_modified.stack_p41, r = 1, g = 2, b = 3) + 
    geom_path(data = p41, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_1.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot parcel 4N 2

# cambio de id !! 8 es igual a 7 
p42 <- subset(parcela4.df, id == 7)
ggRGB(TTC08226_modified.stack_p42, r = 1, g = 2, b = 3) + 
    geom_path(data = p42, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.2") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

parcela 4S

# crop and mask whole area
TTC08350_modified.stack_p4S <- crop(mask(TTC08350_modified.stack, parcela4_2),parcela4_2)

crop parcela 4 S

# requires spatialpolygondataframe
# crop parcela 4N 1
parcela43 <- subset(parcelas, parcelas@data$id %in% c(5))
TTC08335_modified.stack_p43 <- crop(mask(TTC08350_modified.stack_p4S, parcela43), parcela43)
# crop parcela 4N 2
parcela44 <- subset(parcelas, parcelas@data$id %in% c(6))
TTC08335_modified.stack_p44 <- crop(mask(TTC08350_modified.stack_p4S, parcela44), parcela44)
 
# cambio de id !! 8 es igual a 7 
p43 <- subset(parcela4.df, id == 4)
ggRGB(TTC08335_modified.stack_p43, r = 1, g = 2, b = 3) + 
    geom_path(data = p43, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.3") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_3.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
# cambio de id !! 8 es igual a 7 
p44 <- subset(parcela4.df, id == 5)
ggRGB(TTC08335_modified.stack_p44, r = 1, g = 2, b = 3) + 
    geom_path(data = p44, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.4") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_4.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Calculate VIs

p41.VIs <- spectralIndices(TTC08226_modified.stack_p41, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p41.VIs)

p42.VIs <- spectralIndices(TTC08226_modified.stack_p42, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p42.VIs)

p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p43.VIs)

p44.VIs <- spectralIndices(TTC08335_modified.stack_p44, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p44.VIs)

Plot VI one by one

NDVI Parcela 4.1

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

And save

ggsave("figures/parcela4_1_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

MSAVI2 Parcela 4.1

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

And save

ggsave("figures/parcela4_1_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

GNDVI Parcela 4.1

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

ggsave("figures/parcela4_1_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

NDVI Parcela 4.2

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

And save

ggsave("figures/parcela4_2_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

MSAVI2 Parcela 4.2

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

And save

ggsave("figures/parcela4_2_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

GNDVI Parcela 4.2

cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

ggsave("figures/parcela4_2_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Again with p4.3

p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p43.VIs)

NDVI Parcela 4.3

ggR(p43.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_3_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

MSAVI2 Parcela 4.3

ggR(p43.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_3_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

GNDVI Parcela 4.3

ggR(p43.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_3_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

foo

Again with p4.3

p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p43.VIs)

NDVI Parcela 4.3

ggR(p43.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_3_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

MSAVI2 Parcela 4.3

ggR(p43.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_3_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

GNDVI Parcela 4.3

ggR(p43.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_3_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Again with p4.4

p44.VIs <- spectralIndices(TTC08335_modified.stack_p44, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)
plot(p43.VIs)

NDVI Parcela 4.4

ggR(p44.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

ggsave("figures/parcela4_4_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

MSAVI2 Parcela 4.4

ggR(p44.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_4_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

GNDVI Parcela 4.4

ggR(p44.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

Save

ggsave("figures/parcela4_4_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Bind indices in a whole dataframe

library(dplyr)
library(plyr) # Tools for Splitting, Applying and Combining Data
raster_to_df <- function(x) {
  stack(as.data.frame(x))
} # convert raster to dataframe
l<- list(p41 = p41.VIs, p42 = p42.VIs, p43 = p43.VIs, p44 = p44.VIs)
l.df <- lapply(X = l, FUN = raster_to_df) # list of data frames
l.df.VIs <- ldply(l.df ,rbind) # Split list, apply function, and return results in a data frame.

Plot NDVI box-plot

l.df.VIs.NDVI <-  subset(l.df.VIs, ind == "NDVI" )
ggplot(l.df.VIs.NDVI) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()

ggsave("figures/boxplot_p41_p42_p43_p44_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot MSAVI2 box-plot

l.df.VIs.MSAVI2 <-  subset(l.df.VIs, ind == "MSAVI2" )
ggplot(l.df.VIs.MSAVI2) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()

ggsave("figures/boxplot_p41_p42_p43_p44_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot GNDVI box-plot

l.df.VIs.GNDVI <-  subset(l.df.VIs, ind == "GNDVI" )
ggplot(l.df.VIs.GNDVI) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()

ggsave("figures/boxplot_p41_p42_p43_p44_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Plot NDVI, GNDVI MSAVI2 histogram

l.df.VIs.NDVI$title <- "NDVI" # fake
ggplot(l.df.VIs.NDVI, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()

ggsave("figures/histo_p41_p42_p43_p44_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
l.df.VIs.MSAVI2$title = "MSAVI2"
ggplot(l.df.VIs.MSAVI2, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()

ggsave("figures/histo_p41_p42_p43_p44_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
l.df.VIs.GNDVI$title <- "GNDVI" # fake
ggplot(l.df.VIs.GNDVI, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()

ggsave("figures/histo_p41_p42_p43_p44_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)

Analysis

# save p41 layers
writeRaster(stack(p41.VIs), paste("p41_", names(p41.VIs), sep = ''), bylayer=TRUE, format='GTiff')
writeRaster(stack(p42.VIs), paste("p42_", names(p42.VIs), sep = ''), bylayer=TRUE, format='GTiff')
writeRaster(stack(p43.VIs), paste("p42_", names(p43.VIs), sep = ''), bylayer=TRUE, format='GTiff')
Error in .getGDALtransient(x, filename = filename, options = options,  : 
  filename exists; use overwrite=TRUE
---
title: "R Notebook"
output:
  pdf_document: default
  html_notebook: default
---

```{r}
# required libraries

library("RStoolbox")
library("raster")
library("rgdal")
library("ggplot2")
library("SDMTools")
library("png")

```

En este caso se eligieron 2 imágenes cada una con 2 parcelas distintas

```{r}
# load tif using stack function instead raster

TTC08226_modified.stack <- stack("../6_qgis/output/TTC08226_modified.tif")
class(TTC08226_modified.stack)


TTC08350_modified.stack <- stack("../6_qgis/output/TTC08350_modified.tif")
class(TTC08350_modified.stack)
```


```{r}
# load parcela 4
library(rgeos)

parcelas <- readOGR("../6_qgis/input/alamala.kml", "alamala")[0]

nrowdim <- dim(parcelas@data)
parcelas@data$id <- c(rep(1:nrowdim))

# 7 8 N
# 5, 6 S
parcela4 <- subset(parcelas, parcelas@data$id %in% c(5,6, 7, 8))
p4.centroid.df <- as.data.frame(gCentroid(parcela4, byid = TRUE)) # extract to label parcels

class(parcela4)
plot(parcela4)


```


```{r}

# ojo cambio de id entre parcela4 y parcela4.df
parcela4.df <- fortify(parcela4) # to plot with ggplot
head(parcela4.df)
```

```{r}
parcela4_1 <- subset(parcelas, parcelas@data$id %in% c(7,8))
parcela4_1.df <- fortify(parcela4_1) # to plot with ggplot

parcela4_2 <- subset(parcelas, parcelas@data$id %in% c(5,6))
parcela4_2.df <- fortify(parcela4_1) # to plot with ggplot

```



```{r}


# Set all pixels to NA, where bands are 0 (remove black background)
# Check if results are affected
# instead use crop and mask together (ver más adelante)

TTC08226_modified.stack[TTC08226_modified.stack[,] == 0] <- NA


# plot scene using ggRGB (from ggplot and RStoolbox)
ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = parcela4_1, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    geom_text(data = p4.centroid.df[3:4,], aes(label = c("4.1", "4.2") ,y = y, x = x), colour = "white") +
    coord_equal() +
    theme_bw()
```
```{r}
ggsave("figures/parcela4N.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


Plot parcel 4.1 (de norte a sur)

```{r}
p41 <- subset(parcela4.df, id == 6)

ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p41, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```




Plot parcel 4.2

```{r}
p42 <- subset(parcela4.df, id == 7)

ggRGB(TTC08226_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p42, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

```{r}


# Set all pixels to NA, where bands are 0 (remove black background)
# Check if results are affected
# instead use crop and mask together (ver más adelante)

TTC08350_modified.stack[TTC08350_modified.stack[,] == 0] <- NA


# plot scene using ggRGB (from ggplot and RStoolbox)
ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = parcela4_2, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    geom_text(data = p4.centroid.df[1:2,], aes(label = c("4.3", "4.4") ,y = y, x = x), colour = "white") +
    coord_equal() +
    theme_bw()
```



```{r}
ggsave("figures/parcela4S.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


Plot parcel 4.3

```{r}
p43 <- subset(parcela4.df, id == 4)

TTC08350_modified.stack[TTC08350_modified.stack[,] == 0] <- NA


ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3, maxpixels = 2e+05, stretch="none", geom_raster = TRUE) + 
    geom_path(data = p43, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


```{r}
p44 <- subset(parcela4.df, id == 5)

ggRGB(TTC08350_modified.stack, r = 1, g = 2, b = 3) + 
    geom_path(data = p44, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

Extraemos las parcelas

parcela 4N

```{r}
# crop and mask whole area
TTC08226_modified.stack_p4N <- crop(mask(TTC08226_modified.stack, parcela4_1),parcela4_1)

```

Crop parcela 4N

```{r}

# requires spatialpolygondataframe

# crop parcela 4N 1
parcela41 <- subset(parcelas, parcelas@data$id %in% c(7))

TTC08226_modified.stack_p41 <- crop(mask(TTC08226_modified.stack_p4N, parcela41), parcela41)


# crop parcela 4N 2

parcela42 <- subset(parcelas, parcelas@data$id %in% c(8))
TTC08226_modified.stack_p42 <- crop(mask(TTC08226_modified.stack_p4N, parcela42), parcela42)
 

```


Plot parcel 4N 1


```{r}
# cambio de id !! 7 es igual a 6
p41 <- subset(parcela4.df, id == 6)

ggRGB(TTC08226_modified.stack_p41, r = 1, g = 2, b = 3) + 
    geom_path(data = p41, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.1") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

```{r}
ggsave("figures/parcela4_1.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

Plot parcel 4N 2


```{r}

# cambio de id !! 8 es igual a 7 
p42 <- subset(parcela4.df, id == 7)

ggRGB(TTC08226_modified.stack_p42, r = 1, g = 2, b = 3) + 
    geom_path(data = p42, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.2") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```
```{r}
ggsave("figures/parcela4_2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


parcela 4S


```{r}
# crop and mask whole area
TTC08350_modified.stack_p4S <- crop(mask(TTC08350_modified.stack, parcela4_2),parcela4_2)

```

crop parcela 4 S
```{r}

# requires spatialpolygondataframe

# crop parcela 4N 1
parcela43 <- subset(parcelas, parcelas@data$id %in% c(5))

TTC08335_modified.stack_p43 <- crop(mask(TTC08350_modified.stack_p4S, parcela43), parcela43)


# crop parcela 4N 2

parcela44 <- subset(parcelas, parcelas@data$id %in% c(6))
TTC08335_modified.stack_p44 <- crop(mask(TTC08350_modified.stack_p4S, parcela44), parcela44)
 

```


```{r}

# cambio de id !! 8 es igual a 7 
p43 <- subset(parcela4.df, id == 4)

ggRGB(TTC08335_modified.stack_p43, r = 1, g = 2, b = 3) + 
    geom_path(data = p43, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.3") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

````
```{r}
ggsave("figures/parcela4_3.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```



```{r}

# cambio de id !! 8 es igual a 7 
p44 <- subset(parcela4.df, id == 5)

ggRGB(TTC08335_modified.stack_p44, r = 1, g = 2, b = 3) + 
    geom_path(data = p44, aes(x = long, y = lat, group = group), size = 1, col="#fbae3b") +
    labs(x="", y="", title="Parcela 4.4") +
   # coord_equal(ylim = c(min(p11$lat), max(p11$lat)), xlim= c(min(p11$long), max(p11$long))) +
    theme_bw() + 
    theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

```{r}
ggsave("figures/parcela4_4.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


Calculate VIs


```{r}

p41.VIs <- spectralIndices(TTC08226_modified.stack_p41, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p41.VIs)
```

```{r}

p42.VIs <- spectralIndices(TTC08226_modified.stack_p42, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p42.VIs)

```

```{r}

p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p43.VIs)
```

```{r}

p44.VIs <- spectralIndices(TTC08335_modified.stack_p44, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p44.VIs)
```

Plot VI one by one

### NDVI Parcela 4.1

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

And save

```{r}
ggsave("figures/parcela4_1_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

### MSAVI2 Parcela 4.1

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

And save

```{r}
ggsave("figures/parcela4_1_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

### GNDVI Parcela 4.1

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p41.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.1") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

```{r}
ggsave("figures/parcela4_1_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```




### NDVI Parcela 4.2

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

And save

```{r}
ggsave("figures/parcela4_2_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

### MSAVI2 Parcela 4.2

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

And save

```{r}
ggsave("figures/parcela4_2_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

### GNDVI Parcela 4.2

```{r}
cols <- colorRampPalette(c("red", "yellow", "lightgreen"))(length(breaks)-1)
ggR(p42.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title= "Parcela 4.2") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))

```

```{r}
ggsave("figures/parcela4_2_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```



### Again with p4.3

```{r}
p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p43.VIs)
```



### NDVI  Parcela 4.3

```{r}
ggR(p43.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


```{r}
ggsave("figures/parcela4_3_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### MSAVI2 Parcela 4.3

```{r}
ggR(p43.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


Save

```{r}
ggsave("figures/parcela4_3_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### GNDVI Parcela 4.3

```{r}
ggR(p43.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

Save

```{r}
ggsave("figures/parcela4_3_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

foo


### Again with p4.3

```{r}
p43.VIs <- spectralIndices(TTC08335_modified.stack_p43, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p43.VIs)
```



### NDVI  Parcela 4.3

```{r}
ggR(p43.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


```{r}
ggsave("figures/parcela4_3_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### MSAVI2 Parcela 4.3

```{r}
ggR(p43.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


Save

```{r}
ggsave("figures/parcela4_3_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### GNDVI Parcela 4.3

```{r}
ggR(p43.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.3") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

Save

```{r}
ggsave("figures/parcela4_3_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### Again with p4.4

```{r}
p44.VIs <- spectralIndices(TTC08335_modified.stack_p44, green = 3, red=2, nir =1, indices=c("NDVI", "MSAVI2", "GNDVI"))
breaks <- seq(0, 1, by=0.01)

plot(p43.VIs)
```



### NDVI  Parcela 4.4

```{r}
ggR(p44.VIs$NDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


```{r}
ggsave("figures/parcela4_4_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### MSAVI2 Parcela 4.4

```{r}
ggR(p44.VIs$MSAVI2, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```


Save

```{r}
ggsave("figures/parcela4_4_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


### GNDVI Parcela 4.4

```{r}
ggR(p44.VIs$GNDVI, geom_raster = TRUE) +
  labs(x="", y="", title="Parcela 4.4") +
  scale_fill_gradientn(colours=cols,  na.value=NA) + 
  theme_bw() +
  theme(plot.title = element_text(lineheight=.8, face="bold", vjust=1, hjust = 0.5))  # make title bold and add spac

```

Save

```{r}
ggsave("figures/parcela4_4_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

Bind indices in a whole dataframe

```{r}
library(dplyr)
library(plyr) # Tools for Splitting, Applying and Combining Data

raster_to_df <- function(x) {
  stack(as.data.frame(x))
} # convert raster to dataframe


l<- list(p41 = p41.VIs, p42 = p42.VIs, p43 = p43.VIs, p44 = p44.VIs)

l.df <- lapply(X = l, FUN = raster_to_df) # list of data frames


l.df.VIs <- ldply(l.df ,rbind) # Split list, apply function, and return results in a data frame.


```


Plot NDVI box-plot

```{r}
l.df.VIs.NDVI <-  subset(l.df.VIs, ind == "NDVI" )
ggplot(l.df.VIs.NDVI) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()
```

```{r}
ggsave("figures/boxplot_p41_p42_p43_p44_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

Plot MSAVI2 box-plot


```{r}
l.df.VIs.MSAVI2 <-  subset(l.df.VIs, ind == "MSAVI2" )

ggplot(l.df.VIs.MSAVI2) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()
```

```{r}
ggsave("figures/boxplot_p41_p42_p43_p44_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

Plot GNDVI box-plot


```{r}
l.df.VIs.GNDVI <-  subset(l.df.VIs, ind == "GNDVI" )
ggplot(l.df.VIs.GNDVI) + 
  geom_boxplot(aes(x = .id, y = values, colour=.id)) +
  facet_grid(. ~ ind) +
  theme_bw()
```

```{r}
ggsave("figures/boxplot_p41_p42_p43_p44_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


Plot NDVI, GNDVI MSAVI2 histogram

```{r}

l.df.VIs.NDVI$title <- "NDVI" # fake

ggplot(l.df.VIs.NDVI, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()
```

```{r}
ggsave("figures/histo_p41_p42_p43_p44_NDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

```{r}

l.df.VIs.MSAVI2$title = "MSAVI2"

ggplot(l.df.VIs.MSAVI2, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()
```

```{r}
ggsave("figures/histo_p41_p42_p43_p44_MSAVI2.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```

```{r}
l.df.VIs.GNDVI$title <- "GNDVI" # fake


ggplot(l.df.VIs.GNDVI, aes(x = values, colour=.id)) + 
  geom_freqpoly(aes( y=(..count..)/sum(..count..)), binwidth = 0.005) +
  facet_wrap(~title) +
  scale_y_continuous(labels=scales::percent) +
  ylab("relative frequencies") + 
  theme_bw()
```

```{r}
ggsave("figures/histo_p41_p42_p43_p44_GNDVI.png", 
 plot = last_plot(), # or give ggplot object name as in myPlot,
 width = 5, height = 5, 
 units = "in", # other options c("in", "cm", "mm"), 
 dpi = 300)
```


# Analysis

```{r}
library('dplyr')
l.df.VIs %>% group_by(ind) %>% summarise_at("values", funs(mean, median, max, min,sd), na.rm = TRUE)

```

```{r}
library('dplyr')
l.df.VIs %>% group_by(ind, .id) %>% summarise_at("values", funs(mean, median, max, min,sd), na.rm = TRUE)
```

```{r}
saveRDS(l.df.VIs, file = "VIP4.rds")
```


```{r}
# save p41 layers
writeRaster(stack(p41.VIs), paste("p41_", names(p41.VIs), sep = ''), bylayer=TRUE, format='GTiff')
writeRaster(stack(p42.VIs), paste("p42_", names(p42.VIs), sep = ''), bylayer=TRUE, format='GTiff')
writeRaster(stack(p43.VIs), paste("p43_", names(p43.VIs), sep = ''), bylayer=TRUE, format='GTiff')
writeRaster(stack(p44.VIs), paste("p44_", names(p44.VIs), sep = ''), bylayer=TRUE, format='GTiff')

```

